core-ml

Automate Core ML, Vision, and Natural Language integration into Apple platform apps.

Updated Mar 1, 2026

Search Tags:#vision #foundation-models #on-device #model-optimization #coreml #naturallanguage #create-ml

Ready to run

npx skills add https://github.com/mazicimert/RunDom --skill core-ml-mazicimert

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: core-ml
Source: https://github.com/mazicimert/RunDom/tree/main/.claude/skills/core-ml
Command: npx skills add https://github.com/mazicimert/RunDom --skill core-ml-mazicimert

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Core ML, Create ML, Vision, and Natural Language tooling are brought together to simplify on-device ML integration in Apple platform apps, enabling developers to deploy image classification, text analysis, object detection, sound classification, and model optimization with a clear decision path between Core ML and Foundation Models.

Core Features & Use Cases

Simplified integration of Core ML models with Vision and Natural Language frameworks.
On-device training and optimization workflows via Create ML and model quantization/palettization guidance.
Clear decision guidance between Core ML and Foundation Models for different tasks like generation vs. classification.

Quick Start

Provide your ML task and I will generate a Core ML integration plan.

Frequently Asked Questions about core-ml

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I integrate Core ML models for image classification in Apple apps?▼

Core ML integration for image classification is automated by generating configuration files, code templates for model loading, and best practices for performance tuning and error handling within Apple platform apps.

What's the best way to choose between Core ML and Foundation Models for my app?▼

Choosing between Core ML and Foundation Models depends on your task: Core ML suits classification and analysis, while Foundation Models are guided for generation tasks, with clear decision paths provided for each.

How do I optimize on-device machine learning models using Create ML?▼

On-device model optimization is handled through Create ML workflows, providing guidance on quantization and palettization techniques to reduce model size and improve inference performance on Apple devices.

Can I use Vision and Natural Language frameworks together with Core ML?▼

Vision and Natural Language frameworks are integrated with Core ML to simplify on-device deployment for object detection, text analysis, and image classification tasks across Apple platforms.

Does on-device model optimization support sound classification tasks?▼

On-device model optimization and integration support sound classification alongside image classification, text analysis, and object detection, delivering robust error handling and performance tuning for Apple apps.

Why does my Core ML model fail to load on certain Apple devices?▼

Core ML model loading failures are addressed through generated code templates that include robust error handling and performance tuning guidance to ensure compatibility and efficient execution across Apple platforms.